What product feed data do AI shopping assistants use?+
Feeds carry the minimum required fields, attribute values are inconsistent, and the descriptive detail that answers buying questions never makes it in. The practical answer is to diagnose before acting: audit feed completeness and value consistency against the questions buyers ask, then expand attribute coverage and normalise values across the catalogue. Complete, consistent product data across feed, page and markup — the condition for appearing in AI shopping comparisons.
How long does this take to show results?+
Long-tail and comparison questions typically move within 30–90 days of the fix shipping. Broad category questions take longer because they depend on corroboration accumulating outside your own site, which you influence but do not control.
What does it cost?+
The forensic audit is $4,500 and includes the full diagnosis plus a prioritised 90-day plan you can execute yourself. Ongoing engagements are scoped per brand. Implement our plan and if you do not see movement in 3–6 months, we refund our fees in full.
Can we do this in-house?+
Often, yes — and the audit is deliberately written so you can. Most teams have the capability but lack the diagnostic layer and the measurement, which is what makes the work land on the right pages in the right order.
How do you measure success here?+
Attribute completeness rate and product appearance in shopping probe answers. Baselines are captured before anything ships and reported by segment, so improvement is attributable rather than assumed.
Is this different from regular SEO?+
It shares the technical groundwork but changes the target. Classic SEO competes for a position in a list of links; this competes to be the source a model uses and names, which rewards explicit data, honest qualifiers, named expertise and independent corroboration.